Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS

Feng, Liping; Geng, Xiaoyuan; Zhu, A‐Xing; Fraser, Walter; Waddell, Arnie

doi:10.1016/j.geoderma.2011.05.007

Cited by 57 publications

(42 citation statements)

References 22 publications

(25 reference statements)

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“…The key to selecting environmental covariates is that the covariate reflects the spatial variation of soils and at the same time data on spatial variation of this covariate can be easily obtained. With the development of earth observation technologies and spatial analysis method, many new useful covariates are emerging (Nield et al, 2007;Zhu et al (2010b); Liu et al, 2012;Qin et al, 2012). Statistical methods (such as principal components analysis, stepwise regression) could help us select appropriate covariates (Pechenizkiy et al, 2003;Behrens et al, 2010;Samuel-Rose et al, 2015;Ramadan et al, 2001;Mansuy et al, 2014;Beaudoin et al, 2014).…”

Section: Determination Of Prediction Uncertaintymentioning

confidence: 99%

An heuristic uncertainty directed field sampling design for digital soil mapping

et al. 2016

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Legacy samples are a valuable data source for digital soil mapping. However, these sample sets are often small in size and ad hoc in spatial distribution. Constrained by the limited representativeness of such a sample set, the obtained soil maps are often incomplete in spatial coverage with "gaps" at the locations which cannot be well represented by these samples. The maps may also contain areas of high prediction uncertainty. In order to extend the predicted area and reduce prediction uncertainty, additional samples are needed. This paper presents a sampling design based on prediction uncertainty to select samples which will effectively complement the sparse and ad hoc samples, and maximize the spatial coverage of prediction and minimize prediction uncertainty. A case study in China shows that this sampling scheme was effective in achieving these goals. Compared with stratified random sampling scheme, when the number of additional samples is the same, the produced map using uncertainty directed samples has larger predicted area, and the accuracy of the produced map is higher than that of the maps using stratified random samples. The finding of this study suggests that prediction uncertainty is a useful indicator to aid field sample selection and to complement the legacy data. Furthermore, the mapping accuracy produced using this method can be quantitatively related to the number of additional samples needed which opens a new horizon for digital soil mapping.

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Section: Determination Of Prediction Uncertaintymentioning

confidence: 99%

An heuristic uncertainty directed field sampling design for digital soil mapping

et al. 2016

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“…However, it is difficult and expensive to map the spatial distribution of soil texture by field sampling at point scale [12]. In recent years, remote sensing has become an effective way to obtain soil texture data over large scales [13,14].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China

Zhang

et al. 2018

Sustainability

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Accurate mapping the spatial distribution of different soil textures is important for eco-hydrological studies and water resource management. However, it is quite a challenge to map the soil texture in data scarce, hard to access mountainous watersheds. This paper compares a nonlinear method, the Markov chain random field (MCRF) with a classical linear method, ordinary kriging (OK) for calculating the soil texture at different search radiuses in the upstream region of the Heihe River Watershed. Results show that soil texture values that were calculated by the OK method tends to predict soil texture values within a certain range (sand (12.098~40.317), silt (47.847~71.231), and clay (12.781~19.420)) because of the smoothing effect, thus leading to greater accuracy in predicting the major soil texture type (silt loam). Nonetheless, the MCRF method considers the interclass relationships between sampling points, leading to greater accuracy in predicting minor types (loam and sandy loam). Meanwhile, the OK method performed best for all the types at the radius of 65 km influenced by the densities of all the sampling points, while the best performance of the MCRF method differs with radiuses as the largest densities varying for different soil types. For loam and sandy loam, the OK method ignored them, thus the MCRF method is more suitable in mountainous areas with high soil heterogeneity.

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“…In order to capture the changes of soil reflectance from wet to dry for the purpose of differentiating soil conditions, a dynamic feedback pattern for soil was introduced as a new data variable captured by satellite image to study soil characteristics [9]. Previous research has established that the feedback pattern can be used as a new environmental covariate to indicate soil spatial variation over low relief areas [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…With this design, rainfall was considered as an input to land surface. The changes in soil reflectance that occurred in the process of drying after a rain event were considered as feedback in response to the rain [10]. MODIS was chosen because it has a high temporal resolution compared to other hyperspectral sensors.…”

Section: Introductionmentioning

confidence: 99%

“…The approach was applied to a large area of farmland in northeastern Illinois, USA, as a case study. The proposed method effectively fills data-gaps and allows the soil feedback pattern in every pixel to be used as a data source to predict soil types and properties regardless of cloud cover, using a methodology created in previous studies [9] and [10]. …”

Section: Introductionmentioning

confidence: 99%

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Data-Gap Filling to Understand the Dynamic Feedback Pattern of Soil

et al. 2015

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Detailed and accurate information on the spatial variation of soil over low-relief areas is a critical component of environmental studies and agricultural management. Early studies show that the pattern of soil dynamics provides comprehensive information about soil and can be used as a new environmental covariate to indicate spatial variation in soil in low relief areas. In practice, however, data gaps caused by cloud cover can lead to incomplete patterns over a large area. Missing data reduce the accuracy of soil information and make it hard to compare two patterns from different locations. In this study, we introduced a new method to fill data gaps based on historical data. A strong correlation between MODIS OPEN ACCESSRemote Sens. 2015, 7 11802 band 7 and cumulated reference evapotranspiration ( 0 ) has been confirmed by theoretical derivation and by the real data. Based on this correlation, data gaps in MODIS band 7 can be predicted by daily evaporation data. Furthermore, correlations among bands are used to predict soil reflectance in MODIS bands 1-6 from MODIS band 7. A location in northeastern Illinois with a large area of low relief farmland was selected to examine this idea. The results show a good exponential relationship between MODIS band 7 and CET0 0.5 in most locations of the study area (with average R 2 = 0.55, p < 0.001, and average NRMSE 10.40%). A five-fold cross validation shows that the approach proposed in this study captures the regular pattern of soil surface reflectance change in bands 6 and 7 during the soil drying process, with a Normalized Root Mean Square Error (NRMSE) of prediction of 13.04% and 10.40%, respectively. Average NRMSE of bands 1-5 is less than 20%. This suggests that the proposed approach is effective for filling the data gaps from cloud cover and that the method reduces the data collection requirement for understanding the dynamic feedback pattern of soil, making it easier to apply to larger areas for soil mapping.

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Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS

Cited by 57 publications

References 22 publications

An heuristic uncertainty directed field sampling design for digital soil mapping

An heuristic uncertainty directed field sampling design for digital soil mapping

A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China

Data-Gap Filling to Understand the Dynamic Feedback Pattern of Soil

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